Tag Archives: HFT

High-frequency trading can be a tough topic to tackle. Regulators around the globe are scrambling to ensure their markets are fair and orderly while drawing in liquidity and lessening spreads. Greeksoft discusses Indian regulator Sebi’s new proposals targeted at algorithmic and high-frequency trading, and what it could mean for the nation’s burgeoning market going forward.

In North America and Europe, high-frequency trading (HFT) has been put under the microscope. Flash crashes—the most recent of which befell the British pound on October 7—price volatility, heaps of cancelled orders, and the belief that the financial markets are rigged, notably outlined in Michael Lewis’ Flash Boys, has led regulators to reexamine the potential (some might say “theoretical”) benefits of the practice: added liquidity, tighter spreads, and decreased costs.

But one region of the world that has been quick to embrace HFT in order to bring in foreign expertise and liquidity is Asia. To varying degrees, Japan, Singapore, Australia and Hong Kong have seen their exchanges build out their trading platforms and networks to entice low-latency traders to try out their markets. While it’s too early to judge the long-term success or failure of these efforts, there’s clearly an appetite for this type of trading in this region.

Another market that is poised to join this group is India. For those unfamiliar with India’s marketplace, the second-most populous country in the world has two stock exchanges: the Bombay Stock Exchange (BSE) and the National Stock Exchange (NSE).

The topic of HFT brings about its own drama. India’s regulator, the Securities Board and Exchange of India (Sebi), hopes to address those concerns. As such, any conversation about the merits of HFT will first have to begin with how best to regulate algorithmic trading and co-location.

HFT and Algo Differentiation

Earlier in the year, the Indian regulator issued a discussion paper, Strengthening of the Regulatory Framework for Algorithmic Trading & Co-location.

The nine-page report lists seven measures designed to address concerns relating to market quality, market integrity and fairness due to the increased usage of algo trading and co-location in the Indian securities market. By the end of August, Sebi had collected feedback from the industry, including from broker-dealers, investors and intermediaries, among others.

Sebi chief Upendra Kumar (UK) Sinha recently said the regulator would consult all stakeholders—including the Reserve Bank of India (RBI) and technology providers—before making a final decision on implementing new rules targeted specifically at HFT.

There is a concern among HFT proponents that some of the measures proposed will be detrimental to the overall trading landscape in India. According to data provided by Sebi, algorithmic trading and HFT strategies account for about 40 percent of the country’s trades. However, that estimate lumps HFT and algo trading together. While all high-frequency trading strategies are conducted using algorithmic platforms, not all algorithmic trading constitutes HFT. Even in the US, regulators have given up on trying to define an exact latency that constitutes HFT, but there still needs to be some differentiation between HFT and simple algorithmic trading, notes one asset management firm’s risk manager.

New Ideas

In the paper, Sebi states that algo trading and HFT drew regulatory attention due to price volatility, market noise, costs imposed on other market users, and it often presented limited opportunities for regulatory intervention. The regulator is examining several measures to “allay the fear and concern” of unfair and inequitable access to the trading systems of the exchanges.

Potential measures to curb high-frequency trading include a minimum resting time for orders; frequent batch auctions; random speed bumps or delays in order-processing or matching; randomization of orders received during a given period; maximum order message-to-trade ratio requirements; separate queues for co-located and non–co-located orders; and a review of tick-by-tick data feeds.

One big concern, though, is that Sebi has not clearly identified the specific problem it is trying to solve. Without proper clarification, implementing those measures without addressing a specific problem could be detrimental, adding complexity to the overall market structure in India.

Rather than target HFT specifically, cracking down on specific cases would deter any intentional misconduct.This then leaves more options on how to ensure a fair and competitive marketplace. Continuous market supervision or guidelines and enforcement of pre-trade checks and market-abuse monitoring have been proven successful in other markets and could also prove useful here.

But the concern is that the measures that Sebi has proposed will likely make a complicated situation even more complex and confusing. As a vendor, we think India and Korea are some of the most challenging countries in terms of compliance. Regulators in India have always been allowed to review the code of algorithmic firms, which causes intellectual property issues, given that the written code is confidential. This is an issue also being debated in the US with the Commodity Futures Trading Commission’s (CFTC’s) recent source-code provision of Regulation Automated Trading (AT), which stipulates that trading firms have to turn over their source codes to a repository, as opposed to handing over the code after being served a subpoena.

Resting Times

Despite the discussion paper being closed for feedback, Sebi is still consulting with industry bodies and market participants on the proposals as the body looks to get more clarity on the specific issues it intends to solve. And people are happy that they’re taking their time—at least for right now—but the concern of unintended consequences looms.

As an example, one of Sebi’s proposals is to implement a minimum-resting time for orders of 500 milliseconds; the idea is to eliminate “fleeting orders,” or orders that are put in and then cancelled within a short amount of time. But Sebi also noted that no other regulator currently mandates the resting-time mechanism.

Back in March 2013, the Australian Securities and Investment Commission (ASIC) asked for feedback on implementing a similar measure to address concerns market operators and participants had over the effect of HFT and dark-pool trading. However, ASIC later decided against it, saying that such an implementation would only affect about 1 percent of order amendments and 2.26 percent of order deletions. In total, that represented approximately 1.25 percent of all order flows, including executed orders on the Australian market. So they concluded that the reward of such a bold move would not be worth the added market complexity.

“The proposed minimum resting time rule would affect only a small portion of HFT operators. In ASIC Report 331, it is estimated that HFT accounts for 46 percent of orders and 32 percent of trades in the Australian equities market, with around 25 percent to 35 percent of small fleeting orders attributable to high-frequency traders,” according to Capital Markets Consulting, which was commissioned by the Financial Services Council of Australia to conduct research on the impact of technology on capital markets.

Risk manager says implementing a minimum resting time for orders would definitely hurt HFT players, but the source also questions the need for that kind of speed in the first place. “It is such a small time period that the normal institutional investor wouldn’t have a problem with this. But 500 milliseconds is like an eternity to HFT traders. What are you doing that requires that kind of speed and why would you be cancelling orders in half a minute?” he says.

Questions Abound

Another proposed measure is the random speed bumps or delays in order processing and/or matching, similar to what IEX in the US has implemented. IEX introduced a two-way 350 microsecond delay on communications from its members and its trading system.

Sebi said in its discussion paper that this type of mechanism could discourage latency-sensitive strategies, which would drastically affect HFT but would not deter non-algo order flow. The intent behind it is to “nullify the latency advantage of the co-located players to a large extent,” it said.

As for randomization of order matching for co-located orders and non–co-located orders, implementing such measures may result in shifting the problem rather than resolving it.

While Sebi mulls over whether it should implement some, if any, of the proposed measures, there is no doubt that any of these measures would affect liquidity in India’s market—the question is, whether the impact would be positive or negative.

We understand that at least some of the proposals will be implemented, since Sebi put out the discussion paper. The industry understands that Sebi is under pressure to implement measures to solve what it perceives to be a problem. These proposals, if implemented, may impact volumes and, hence, liquidity.

The Liquidity Issue

Whether in Asia, North America or Europe, the HFT battle often comes down to the question of liquidity. Access to liquidity in Asia has always been complicated because the region is both fragmented—each country has its own market structures/rules and the differentiation isn’t always that clear—and homogenous, as most markets have only one or two exchanges. Other than Japan and China, markets in Asia are really small. US markets trade around $250 billion a day while markets in Asia trade only about a third of that. Trying to trade a $50 million block in India is very hard; you would exhaust liquidity and end up pushing up prices. So the more liquidity you have, the better.

This is a big reason why countries like Japan, Singapore and Australia have tried to entice HFT firms, which boosts volumes.

The main benefit of HFT would be the creation of more liquidity, if it is hard to buy and sell, then institutional investors will see the risk of being stuck with positions longer than they want. This is definitely not the only factor in question for encouraging institutional investment, but anything that helps tighten spreads and improve liquidity is important.

Salient Points

The Securities and Exchange Board of India (Sebi) is mulling implementing measures to address concerns around HFT.

Some industry participants have criticized Sebi for not specifying the exact problem it is trying to solve, and they believe that implementing any of the proposed measures would adversely affect liquidity in India.

For all the talk of HFT, the practice has seemed to have plateaued in Asia, so is it best to let the market work itself out before adding in any new rules that could bring about unintended consequences and complexity.

Note: As we are writing this blog, it was reported by Mint, a business newspaper, that Sebi will start a second round of consultation—following the August consultation paper—and it will drop some of its earlier proposals, though details of what will be removed were not released as of deadline.

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With increasing volatility in the equity markets and stiffening competition among brokerages, more and more institutions are adopting technology to use algorithmic and high frequency trading (HFT) to stay in the race.

According to stock exchange data, algorithmic trading accounted for 14.94% and 20.78% of total cash market turnover on BSE and NSE, respectively, in February 2013.

According to BSE, the share of algo trading has never been so high before.

While NSE declined to share the historical data related to the share of algo trading, information on BSE website clearly shows that algos have gained immense popularity in the recent months. For instance, algo accounted for less than 10% of the total turnover in December last year and stayed in the range of 5-8% for most part of 2012.

Algo trading refers to the use of computer programs to execute trades in the stock, commodity or other financial markets. These programs execute trades as and when the pre-defined parameters related to price, timing, quantity are triggered. Complex trading strategies can also be implemented using algos.

Market experts say that technology — by way of algo-based trading and HFT — continues to play a big role in changing the brokerage industry as it helps in executing orders in fraction of a second with utmost efficiency, accuracy and without human intervention.

“Technology is playing a huge role. It might replace human beings one day, although not completely. If the same task could done with the help of a machine in more efficient and time saving manner, why would you not invest in it?”

Market experts said that these software have advanced in such a way that one can do derivatives rollover by a click of a button. “Earlier a dealer had to sit in front of the screen and manually feed the order. It was time-consuming and a costly affair”.

“Proprietary desk of international brokerages the world over wanted DMA into Indian equities so that they could punch their orders using algos without the need of a broker in India…While the concerns raised by the market regulator are appreciated, I do not think the regulator can take a step back.”

The RBI had highlighted the risks attached to algorithmic trading in its June 2012 Financial Stability Report. The report stated that several instances of extreme volatility and disruptions were witnessed in Indian stock markets that could be directly and indirectly attributed to the increased use of algorithmic trading.

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Using Algorithmic Trading and High Frequency Trades could boost your trading options and perhaps your profitability too. In fact it is attracting increasing attention among market players ever since the regulator SEBI has permitted its use on exchanges .Lets analyse how technology could help you gain an edge on the markets.

Do you remember all of those slick Hollywood movies like Sneakers, Virtuosity or The Net where people use technology to gain an edge; usually monetary; over their rivals? The other day we got a peek into just such a world that at the very least left us breathless! But, at the end of the day remember that technology is a tool and it is only as good as you are!

Algorithmic Trade per se is nothing but a reflection of what happens in our brains. Algorithmic trading, also called automated trading, black-box trading, or Algo trading, is the use of electronic platforms for entering trading orders with an algorithm which executes pre-programmed trading instructions whose variables may include timing, price, or quantity of the order, or in many cases initiating the order without human intervention.

God has given us the ability , we only do this with computer procedures in a more organised or swifter manner than what God has given us . Within this broader genre exist the option of High Frequency Trades. This is basically trading that is done several times in one day, Intraday traders are High Frequency Traders. Some traders trade over a thousand times a day, and it is here that you need high speed programmes to generate your trades.

The simplified process in High Frequency Trades would start from Trade generation, through trade routing till the final step of trade execution.

You may not get the trade at the price that you expect, but you would get it at the best price in the market, this explains about the dynamics of the equity markets vis-à-vis High Frequency Trades.

Here is where the high-tech dazzle comes in. The speed of trades (in exchanges) is such is that if an exchange offers space in the exchange for your server, then you have a time advantage. Thus even the time that is taken to bounce a (trade) signal off a satellite can be avoided. While this may not be a huge issue if you were to trade say just 500 or so times a day, equations change if you trade say a million times a day.

So if there are two exchanges where there is a differential in speeds then an arbitrage opportunity exists to make money. “Arbitrage opportunities exist across time and space,”

High Frequency Trades is about volumes and not margins. Basically it is about thousands of trade and the arbitrage opportunities that lie thereof. At a personal level we feel that High Frequency Trades help in improving efficiencies in markets.

But again as said, we need to use the human mind and not technology to make money here. In High Frequency Trades you trade very fast to make money, while in Algorithmic Trading you use strategy to make money. All High Frequency Trades is Algo Trading, but not all Algo Trading is High Frequency Trades.

Algo Trading is thus, a mathematical model to trade, i.e. the timing, submission and the management of trade orders. In Dubai for example this model supports some 65% of the trading activity and in India this accounts for some 20% of trading activity in the equities arena.

And just in case one felt that the Algo model resulted in volatility in the equities segment, we must not forget that this mode of trading is just a tool and that computers and their systems just obey orders that are placed by humans. Algorithmic trading is not just a facility but and aid.

So you can use the Algorithmic system either to automatically execute a trade or as a decision support mechanism. While Algorithmic trading gives you freedom to trade, it does not replace fundamental research. It only enhances trading efficiency.

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Algorithmic trading is all the rage in India right now, and across the market the view is unanimous: the only way is up. The question is how just fast it will grow.

By the start of 2012, Algo accounted for some 24 percent of cash equities turnover in India and about 30 percent of equity derivatives. According to figures from the Bombay Stock Exchange, by far the smaller of the two Indian exchanges that dominate equities trading, the share for equity derivatives has already jumped to 45 percent since then.

Algorithms and High frequency trading are the hottest topics in the market – algorithmic trading and HFT itself, and now the regulations around it. This is what the majority of players in the market are focused on today.

India has the building blocks in place for a ramp-up. Co-location has been available from both the Bombay Stock Exchange and its bigger competitor, the National Stock Exchange, for 18 months. Both exchanges, and market observers, say their trading platforms can handle HFT. Direct market access is available. Smart order routing between the two exchanges has also been operating since August 2010.

The Indian regulator, the Securities and Exchange Board of India (SEBI), produced guidelines for algorithmic trading in March which brokers, exchanges and market watchers hail as a sensible response. The new rules, they say, recognise that algorithmic trading is a natural development and are aimed at preventing problems but not blocking growth.

“All the dynamics point to an increase in automated and algo trading in the next few years,” Expect the cash equities and derivatives levels to raise around 50-55 percent within the next year or so.

Algorithms – step-by-step mathematical procedures – generate automatic trades, conducted by computers, each one racing to be first. And while some computers do receive news about the outside world in electronic format, many high-frequency trading algorithms are simply responding to the hectic world of the electronic trading floor.

Humans still watch the systems, but the computers move far too quickly for us to react to everything they do. To give you a sense of how fast high-frequency trading can be, in the time it takes Usain Bolt to react to the starting pistol, a high-frequency trading platform could complete about 165,000 separate trades.

Now this isn’t quite as insane as it sounds. These computers, all competing with each other, are a lot cheaper and more efficient than human traders trying to match bids to buy and offers to sell. So within reason, automated, high-frequency trading is a good thing. But it’s possible to have too much of a good thing.

High-Frequency Trading are divided into five categories:

First, there are algorithms designed not to lose money while executing a trade that’s been placed by a human. If you try to buy a large block of shares all at once, for instance, you might find that there aren’t enough potential sellers and you’ll have to wait for others to show up.

Other computers may see that you’ve got this large unfilled order and exploit it, perhaps by snapping up shares and selling to you at a profit. To avoid this problem you can ask a computer to slice up your big trade into smaller, more subtle pieces.

Then there are algorithms designed simply to make money by finding buyers and sellers with a little margin between them.

Third, there are algorithms which find statistical relationships between different shares or bonds, and when the statistical relationship fails to hold – even for a moment – they jump in and make a bet that normal service will be resumed. These are called statistical arbitrage algorithms.

So far, so good – it would be hard to find many people in finance who would consider these three types of high-frequency trading to be immoral.

But there are two rather more predatory strategies. One is called algo-sniffing. Here, a super-fast computer tries to find other computers going about their everyday business of buying or selling shares, and figures out what they’re going to do and when.
The algo-sniffer can then get ahead of the game and exploit the slower computer. And of course you could have algo-sniffer-sniffers and algo-sniffer-sniffer-sniffers in a high-frequency arms race. No wonder speed can be so important.

And finally, a particular sub-category of the algo-sniffer is the spoofer, which deliberately makes fake offers designed to lure other computers to show their hands, then cancels the offers. Spoofing might be illegal, or at least against the rules of stock exchanges, but it’s hard to prove that it’s going on.

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High Frequency Trading or HFT is a form of automated trading which cashes in on fleeting market opportunities through a deluge of orders executed in fractions of a second. Positions may be held only for minutes, the ‘portfolio’ is churned furiously and no position is carried overnight!

Given that they are chasing minuscule gains from high volumes, high-speed traders gain by shortening their execution time to a few milli-seconds.

Stock exchanges around the world (and in India too) have actively aided and abetted HFT by allowing market players to rent ‘co-location’ facilities at the exchange itself.

With their terminals huddled close to the exchange’s servers, co-located members — through fibre optic connectivity — execute trades at a fraction of the time that others do.

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Equity markets have entered an era where machines are rapidly replacing humans. Programme-driven trades account for around 70 per cent of equity trading in the US and for 40 per cent in Europe. We in India are not far behind with one-third of trades in both cash and derivative segments of the National Stock Exchange driven by such programmed orders.

While many of these programmes use algorithms that execute orders through the day, speed is vital in one subset of algo trading known as high-frequency trading (HFT). In HFT, the programme that smells out opportunities and executes them the fastest, scores. Execution time is measured in milliseconds, or one-thousandth of a second. One buy and sell transaction could take just 10 milliseconds and in the race for being the fastest, traders are moving their terminals as close to the exchange servers as possible.